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Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Audi e-tron - more cool rear lights from @Audi" STOCK: nan DATE: nan
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan
The stock shows a neutral return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan
Predicted 1_DAY_RETURN: nan Predicted 2_DAY_RETURN: nan Predicted 7_DAY_RETURN: nan
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Audi" STOCK: 18/09/2018 DATE: 740.0
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.425 and the TextBlob polarity score is @Audi.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: 18/09/2018 1_DAY_RETURN: -0.0135135135135135 2_DAY_RETURN: -0.0135135135135135 3_DAY_RETURN: -0.0081081081081081 7_DAY_RETURN: 71.0
The stock shows a consistent positive return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: 18/09/2018 LAST_PRICE: -0.0054054054054054 PX_VOLUME: 36.491 VOLATILITY_10D: 23.779 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.425 TEXTBLOB_POLARITY: @Audi
Predicted 1_DAY_RETURN: -0.0135135135135135 Predicted 2_DAY_RETURN: -0.0135135135135135 Predicted 7_DAY_RETURN: 71.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @Reuters: Audi launches electric SUV in Tesla's backyard, with assist from Amazon https://t.co/dNnvPLq69r " STOCK: Amazon DATE: 18/09/2018
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 1 and the TextBlob polarity score is 0.0.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: Amazon 1_DAY_RETURN: -0.0170114113495273 2_DAY_RETURN: 0.0150124932381958 3_DAY_RETURN: 0.0150124932381958 7_DAY_RETURN: 0.0237500321990675
The stock shows a consistent positive return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: Amazon LAST_PRICE: 1941.05 PX_VOLUME: 4268706.0 VOLATILITY_10D: 27.565 VOLATILITY_30D: 22.124 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.0
Predicted 1_DAY_RETURN: -0.0170114113495273 Predicted 2_DAY_RETURN: 0.0150124932381958 Predicted 7_DAY_RETURN: 0.0237500321990675
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Are Google ranking factors studies just plain dumb? #GoogleRankingFactor @GoogleTrends @Google @googleanalytics https://t.co/hBAAIXobSJ " STOCK: Google DATE: 18/09/2018
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is -1 and the TextBlob polarity score is -0.29464285714285715.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: Google 1_DAY_RETURN: -0.0062376297007137 2_DAY_RETURN: 0.0093136036877416 3_DAY_RETURN: 0.0093136036877416 7_DAY_RETURN: 0.0196039790593861
The stock shows a consistent positive return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: Google LAST_PRICE: 1167.11 PX_VOLUME: 1615701.0 VOLATILITY_10D: 16.992 VOLATILITY_30D: 16.753 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: -0.29464285714285715
Predicted 1_DAY_RETURN: -0.0062376297007137 Predicted 2_DAY_RETURN: 0.0093136036877416 Predicted 7_DAY_RETURN: 0.0196039790593861
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "@HarvardBiz Google Pixel Fraud might happen again with the launch of Pixel 3 on 9 October 2018 @Google is Killing.… https://t.co/IG6704n0Ob " STOCK: Google DATE: 18/09/2018
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 1 and the TextBlob polarity score is 0.0.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: Google 1_DAY_RETURN: -0.0062376297007137 2_DAY_RETURN: 0.0093136036877416 3_DAY_RETURN: 0.0093136036877416 7_DAY_RETURN: 0.0196039790593861
The stock shows a consistent positive return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: Google LAST_PRICE: 1167.11 PX_VOLUME: 1615701.0 VOLATILITY_10D: 16.992 VOLATILITY_30D: 16.753 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.0
Predicted 1_DAY_RETURN: -0.0062376297007137 Predicted 2_DAY_RETURN: 0.0093136036877416 Predicted 7_DAY_RETURN: 0.0196039790593861
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "@nextofficial dreadful customer service from Next... I don’t see why I should be punished for a courier mistake. No… https://t.co/t5RbCwbauo" STOCK: nan DATE: nan
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan
The stock shows a neutral return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan
Predicted 1_DAY_RETURN: nan Predicted 2_DAY_RETURN: nan Predicted 7_DAY_RETURN: nan
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Next" STOCK: 18/09/2018 DATE: 5340.0
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is -0.5 and the TextBlob polarity score is @nextofficial.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: 18/09/2018 1_DAY_RETURN: 0.0018726591760299 2_DAY_RETURN: 0.0018726591760299 3_DAY_RETURN: 0.0157303370786516 7_DAY_RETURN: 476980.0
The stock shows a consistent positive return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: 18/09/2018 LAST_PRICE: 0.0059925093632958 PX_VOLUME: 11.929 VOLATILITY_10D: 14.738 VOLATILITY_30D: -1.0 LSTM_POLARITY: -0.5 TEXTBLOB_POLARITY: @nextofficial
Predicted 1_DAY_RETURN: 0.0018726591760299 Predicted 2_DAY_RETURN: 0.0018726591760299 Predicted 7_DAY_RETURN: 476980.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "@pipsmithe @Apple I've blocked Apple a while ago :)" STOCK: nan DATE: nan
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan
The stock shows a neutral return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan
Predicted 1_DAY_RETURN: nan Predicted 2_DAY_RETURN: nan Predicted 7_DAY_RETURN: nan
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Apple" STOCK: 18/09/2018 DATE: 218.24
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.5 and the TextBlob polarity score is @Apple.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: 18/09/2018 1_DAY_RETURN: 0.0256598240469208 2_DAY_RETURN: 0.0256598240469208 3_DAY_RETURN: 0.0257056451612902 7_DAY_RETURN: 31571712.0
The stock shows a consistent positive return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: 18/09/2018 LAST_PRICE: -0.0016495601173021 PX_VOLUME: 28.915 VOLATILITY_10D: 19.63 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.5 TEXTBLOB_POLARITY: @Apple
Predicted 1_DAY_RETURN: 0.0256598240469208 Predicted 2_DAY_RETURN: 0.0256598240469208 Predicted 7_DAY_RETURN: 31571712.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @HSBC: Watch #entrepreneur @joshtetrick, co-founder of social enterprise @justforall speak to HSBC Private Banking about how they're usi… " STOCK: HSBC DATE: 18/09/2018
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is -1 and the TextBlob polarity score is 0.016666666666666666.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: HSBC 1_DAY_RETURN: 0.0073778051029817 2_DAY_RETURN: 0.0119889332923454 3_DAY_RETURN: 0.0119889332923454 7_DAY_RETURN: 0.0003074085459574
The stock shows a consistent positive return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: HSBC LAST_PRICE: 650.6 PX_VOLUME: 20137068.0 VOLATILITY_10D: 10.363 VOLATILITY_30D: 11.167 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.016666666666666666
Predicted 1_DAY_RETURN: 0.0073778051029817 Predicted 2_DAY_RETURN: 0.0119889332923454 Predicted 7_DAY_RETURN: 0.0003074085459574
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @Eftauzzaman: @facebook Dear Facebook , Everytime I try to open my Facebook account it got disable. I don't know why you guys think that… " STOCK: Facebook DATE: 18/09/2018
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 1 and the TextBlob polarity score is 0.0.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: Facebook 1_DAY_RETURN: 0.0017467248908297 2_DAY_RETURN: 0.0126013724266998 3_DAY_RETURN: 0.0126013724266998 7_DAY_RETURN: 0.0351840299438551
The stock shows a consistent positive return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: Facebook LAST_PRICE: 160.3 PX_VOLUME: 22465236.0 VOLATILITY_10D: 22.219 VOLATILITY_30D: 20.115 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.0
Predicted 1_DAY_RETURN: 0.0017467248908297 Predicted 2_DAY_RETURN: 0.0126013724266998 Predicted 7_DAY_RETURN: 0.0351840299438551
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @trilearning: https://t.co/IsS8FFWJz9: New Releases - Amazon Devices https://t.co/CcChEosLll via @amazon" STOCK: nan DATE: nan
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan
The stock shows a neutral return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan
Predicted 1_DAY_RETURN: nan Predicted 2_DAY_RETURN: nan Predicted 7_DAY_RETURN: nan
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Amazon" STOCK: 18/09/2018 DATE: 1941.05
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.13636363636363635 and the TextBlob polarity score is @amazon.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: 18/09/2018 1_DAY_RETURN: 0.0150124932381958 2_DAY_RETURN: 0.0150124932381958 3_DAY_RETURN: 0.0237500321990675 7_DAY_RETURN: 4268706.0
The stock shows a consistent positive return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: 18/09/2018 LAST_PRICE: -0.0170114113495273 PX_VOLUME: 27.565 VOLATILITY_10D: 22.124 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.13636363636363635 TEXTBLOB_POLARITY: @amazon
Predicted 1_DAY_RETURN: 0.0150124932381958 Predicted 2_DAY_RETURN: 0.0150124932381958 Predicted 7_DAY_RETURN: 4268706.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Just saw this on Amazon: L200BS-1 by Lalapao https://t.co/4GeHrCGGDo via @amazon" STOCK: nan DATE: nan
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan
The stock shows a neutral return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan
Predicted 1_DAY_RETURN: nan Predicted 2_DAY_RETURN: nan Predicted 7_DAY_RETURN: nan
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Amazon" STOCK: 18/09/2018 DATE: 1941.05
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.0 and the TextBlob polarity score is @amazon.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: 18/09/2018 1_DAY_RETURN: 0.0150124932381958 2_DAY_RETURN: 0.0150124932381958 3_DAY_RETURN: 0.0237500321990675 7_DAY_RETURN: 4268706.0
The stock shows a consistent positive return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: 18/09/2018 LAST_PRICE: -0.0170114113495273 PX_VOLUME: 27.565 VOLATILITY_10D: 22.124 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @amazon
Predicted 1_DAY_RETURN: 0.0150124932381958 Predicted 2_DAY_RETURN: 0.0150124932381958 Predicted 7_DAY_RETURN: 4268706.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "@cherishmoonlix @felicitybjc @Apple But then again neither one of you have Apple Music sooo" STOCK: nan DATE: nan
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan
The stock shows a neutral return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan
Predicted 1_DAY_RETURN: nan Predicted 2_DAY_RETURN: nan Predicted 7_DAY_RETURN: nan
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Apple" STOCK: 18/09/2018 DATE: 218.24
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.0 and the TextBlob polarity score is @Apple.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: 18/09/2018 1_DAY_RETURN: 0.0256598240469208 2_DAY_RETURN: 0.0256598240469208 3_DAY_RETURN: 0.0257056451612902 7_DAY_RETURN: 31571712.0
The stock shows a consistent positive return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: 18/09/2018 LAST_PRICE: -0.0016495601173021 PX_VOLUME: 28.915 VOLATILITY_10D: 19.63 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Apple
Predicted 1_DAY_RETURN: 0.0256598240469208 Predicted 2_DAY_RETURN: 0.0256598240469208 Predicted 7_DAY_RETURN: 31571712.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @Burberry: Burberry Pull-Overs, circa 1940 Archive inspiration curated by Riccardo Tisci #BurberryHeritage https://t.co/9HupOlxP9T " STOCK: Burberry DATE: 18/09/2018
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 1 and the TextBlob polarity score is 0.0.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: Burberry 1_DAY_RETURN: -0.0098591549295774 2_DAY_RETURN: 0.0107981220657277 3_DAY_RETURN: 0.0107981220657277 7_DAY_RETURN: -0.0220657276995305
The stock shows a consistent negative return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: Burberry LAST_PRICE: 2130.0 PX_VOLUME: 1579736.0 VOLATILITY_10D: 28.236 VOLATILITY_30D: 27.451 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.0
Predicted 1_DAY_RETURN: -0.0098591549295774 Predicted 2_DAY_RETURN: 0.0107981220657277 Predicted 7_DAY_RETURN: -0.0220657276995305
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Just saw this on Amazon: Dolce & Gabbana Light Blue By Dolce & Gabba... by Dolce & Gabbana for $53.93 https://t.co/Inzb7V4ekj via @amazon" STOCK: nan DATE: nan
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan
The stock shows a neutral return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan
Predicted 1_DAY_RETURN: nan Predicted 2_DAY_RETURN: nan Predicted 7_DAY_RETURN: nan
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Amazon" STOCK: 18/09/2018 DATE: 1941.05
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.2 and the TextBlob polarity score is @amazon.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: 18/09/2018 1_DAY_RETURN: 0.0150124932381958 2_DAY_RETURN: 0.0150124932381958 3_DAY_RETURN: 0.0237500321990675 7_DAY_RETURN: 4268706.0
The stock shows a consistent positive return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: 18/09/2018 LAST_PRICE: -0.0170114113495273 PX_VOLUME: 27.565 VOLATILITY_10D: 22.124 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.2 TEXTBLOB_POLARITY: @amazon
Predicted 1_DAY_RETURN: 0.0150124932381958 Predicted 2_DAY_RETURN: 0.0150124932381958 Predicted 7_DAY_RETURN: 4268706.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @JoyBusinessGH: Nissan to establish an assembling plant in Ghana https://t.co/KgFLgmfmbB @Nissan #JoyBusiness https://t.co/a4PuuhS4lP " STOCK: Nissan DATE: 18/09/2018
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is -1 and the TextBlob polarity score is 0.0.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: Nissan 1_DAY_RETURN: -0.0130353817504655 2_DAY_RETURN: -0.0130353817504655 3_DAY_RETURN: -0.0130353817504655 7_DAY_RETURN: -0.0349162011173184
The stock shows a consistent negative return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: Nissan LAST_PRICE: 1074.0 PX_VOLUME: 17998800.0 VOLATILITY_10D: 17.025 VOLATILITY_30D: 14.452 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.0
Predicted 1_DAY_RETURN: -0.0130353817504655 Predicted 2_DAY_RETURN: -0.0130353817504655 Predicted 7_DAY_RETURN: -0.0349162011173184
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @padraichalpin: Ireland collects disputed Apple taxes in full ahead of appeal https://t.co/nKyK5hjdcP via @Reuters" STOCK: nan DATE: nan
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan
The stock shows a neutral return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan
Predicted 1_DAY_RETURN: nan Predicted 2_DAY_RETURN: nan Predicted 7_DAY_RETURN: nan
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Apple" STOCK: 18/09/2018 DATE: 218.24
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.35 and the TextBlob polarity score is @Reuters.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: 18/09/2018 1_DAY_RETURN: 0.0256598240469208 2_DAY_RETURN: 0.0256598240469208 3_DAY_RETURN: 0.0257056451612902 7_DAY_RETURN: 31571712.0
The stock shows a consistent positive return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: 18/09/2018 LAST_PRICE: -0.0016495601173021 PX_VOLUME: 28.915 VOLATILITY_10D: 19.63 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.35 TEXTBLOB_POLARITY: @Reuters
Predicted 1_DAY_RETURN: 0.0256598240469208 Predicted 2_DAY_RETURN: 0.0256598240469208 Predicted 7_DAY_RETURN: 31571712.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "“Believe in something, Even if it means sacrificing everything” - @Nike Nike Baby 💪🏽 https://t.co/8b3iWvp4YH " STOCK: Nike DATE: 18/09/2018
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is -1 and the TextBlob polarity score is 0.0.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: Nike 1_DAY_RETURN: -0.0234576589256392 2_DAY_RETURN: -0.0207600281491908 3_DAY_RETURN: -0.0207600281491908 7_DAY_RETURN: -0.0308468214872156
The stock shows a consistent negative return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: Nike LAST_PRICE: 85.26 PX_VOLUME: 7277700.0 VOLATILITY_10D: 15.386 VOLATILITY_30D: 18.916 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.0
Predicted 1_DAY_RETURN: -0.0234576589256392 Predicted 2_DAY_RETURN: -0.0207600281491908 Predicted 7_DAY_RETURN: -0.0308468214872156
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "@NDWVnews @ATFHQ Google Pixel Fraud might happen again with the launch of Pixel 3 on 9 October 2018 @Google is Kil… https://t.co/52tsgLjMJZ " STOCK: Google DATE: 18/09/2018
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 1 and the TextBlob polarity score is 0.0.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: Google 1_DAY_RETURN: -0.0062376297007137 2_DAY_RETURN: 0.0093136036877416 3_DAY_RETURN: 0.0093136036877416 7_DAY_RETURN: 0.0196039790593861
The stock shows a consistent positive return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: Google LAST_PRICE: 1167.11 PX_VOLUME: 1615701.0 VOLATILITY_10D: 16.992 VOLATILITY_30D: 16.753 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.0
Predicted 1_DAY_RETURN: -0.0062376297007137 Predicted 2_DAY_RETURN: 0.0093136036877416 Predicted 7_DAY_RETURN: 0.0196039790593861
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "@TechCrunch Google Pixel Fraud might happen again with the launch of Pixel 3 on 9 October 2018 @Google is Killing.… https://t.co/oDu3x20VWw " STOCK: Google DATE: 18/09/2018
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is -1 and the TextBlob polarity score is 0.0.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: Google 1_DAY_RETURN: -0.0062376297007137 2_DAY_RETURN: 0.0093136036877416 3_DAY_RETURN: 0.0093136036877416 7_DAY_RETURN: 0.0196039790593861
The stock shows a consistent positive return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: Google LAST_PRICE: 1167.11 PX_VOLUME: 1615701.0 VOLATILITY_10D: 16.992 VOLATILITY_30D: 16.753 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.0
Predicted 1_DAY_RETURN: -0.0062376297007137 Predicted 2_DAY_RETURN: 0.0093136036877416 Predicted 7_DAY_RETURN: 0.0196039790593861
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Next week at this time #DF18 what what!!! @salesforce @c13moore @cloudgirlninja see you there!!!" STOCK: nan DATE: nan
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan
The stock shows a neutral return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan
Predicted 1_DAY_RETURN: nan Predicted 2_DAY_RETURN: nan Predicted 7_DAY_RETURN: nan
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Next" STOCK: 18/09/2018 DATE: 5340.0
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.0 and the TextBlob polarity score is @salesforce.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: 18/09/2018 1_DAY_RETURN: 0.0018726591760299 2_DAY_RETURN: 0.0018726591760299 3_DAY_RETURN: 0.0157303370786516 7_DAY_RETURN: 476980.0
The stock shows a consistent positive return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: 18/09/2018 LAST_PRICE: 0.0059925093632958 PX_VOLUME: 11.929 VOLATILITY_10D: 14.738 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @salesforce
Predicted 1_DAY_RETURN: 0.0018726591760299 Predicted 2_DAY_RETURN: 0.0018726591760299 Predicted 7_DAY_RETURN: 476980.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Check out Mickey Mouse Patchwork Madras Plaid Wallet Trifold Fabric Disney Parks https://t.co/UojA5IrpsV @eBay" STOCK: nan DATE: nan
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan
The stock shows a neutral return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan
Predicted 1_DAY_RETURN: nan Predicted 2_DAY_RETURN: nan Predicted 7_DAY_RETURN: nan
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Disney" STOCK: 18/09/2018 DATE: 109.53
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.0 and the TextBlob polarity score is @eBay.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: 18/09/2018 1_DAY_RETURN: -0.0024650780608052 2_DAY_RETURN: -0.0024650780608052 3_DAY_RETURN: 0.0006390943120605 7_DAY_RETURN: 4937821.0
The stock shows a consistent positive return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: 18/09/2018 LAST_PRICE: -0.0015520861864329 PX_VOLUME: 12.037 VOLATILITY_10D: 11.133 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @eBay
Predicted 1_DAY_RETURN: -0.0024650780608052 Predicted 2_DAY_RETURN: -0.0024650780608052 Predicted 7_DAY_RETURN: 4937821.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "@Honda Damage on the vehicle was so high that the vehicle became unusable.I found a call to the Honda call center (… https://t.co/mMl5UtxLqY" STOCK: nan DATE: nan
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan
The stock shows a neutral return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan
Predicted 1_DAY_RETURN: nan Predicted 2_DAY_RETURN: nan Predicted 7_DAY_RETURN: nan
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Honda" STOCK: 18/09/2018 DATE: 3284.0
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.03 and the TextBlob polarity score is @Honda.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: 18/09/2018 1_DAY_RETURN: -0.0204019488428745 2_DAY_RETURN: -0.0204019488428745 3_DAY_RETURN: -0.0334957369062119 7_DAY_RETURN: 6561600.0
The stock shows a consistent positive return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: 18/09/2018 LAST_PRICE: -0.0204019488428745 PX_VOLUME: 21.306 VOLATILITY_10D: 21.537 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.03 TEXTBLOB_POLARITY: @Honda
Predicted 1_DAY_RETURN: -0.0204019488428745 Predicted 2_DAY_RETURN: -0.0204019488428745 Predicted 7_DAY_RETURN: 6561600.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @OfficialMonstaX: @Starbucks @hyungnyan [#형원] sips Starbucks like me. https://t.co/np3lDcGgjB" STOCK: nan DATE: nan
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan
The stock shows a neutral return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan
Predicted 1_DAY_RETURN: nan Predicted 2_DAY_RETURN: nan Predicted 7_DAY_RETURN: nan
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Starbucks" STOCK: 18/09/2018 DATE: 55.07
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.0 and the TextBlob polarity score is @Starbucks.